Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence
Abstract Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which expli...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Psychology |
| Online Access: | https://doi.org/10.1038/s44271-025-00190-0 |
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| _version_ | 1832585409970831360 |
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| author | John P. Veillette Howard C. Nusbaum |
| author_facet | John P. Veillette Howard C. Nusbaum |
| author_sort | John P. Veillette |
| collection | DOAJ |
| description | Abstract Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions. |
| format | Article |
| id | doaj-art-288c4f067cf44e0394cf51095a5def88 |
| institution | Kabale University |
| issn | 2731-9121 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Psychology |
| spelling | doaj-art-288c4f067cf44e0394cf51095a5def882025-01-26T12:51:05ZengNature PortfolioCommunications Psychology2731-91212025-01-013111610.1038/s44271-025-00190-0Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalenceJohn P. Veillette0Howard C. Nusbaum1Department of Psychology, University of ChicagoDepartment of Psychology, University of ChicagoAbstract Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.https://doi.org/10.1038/s44271-025-00190-0 |
| spellingShingle | John P. Veillette Howard C. Nusbaum Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence Communications Psychology |
| title | Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence |
| title_full | Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence |
| title_fullStr | Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence |
| title_full_unstemmed | Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence |
| title_short | Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence |
| title_sort | bayesian p curve mixture models as a tool to dissociate effect size and effect prevalence |
| url | https://doi.org/10.1038/s44271-025-00190-0 |
| work_keys_str_mv | AT johnpveillette bayesianpcurvemixturemodelsasatooltodissociateeffectsizeandeffectprevalence AT howardcnusbaum bayesianpcurvemixturemodelsasatooltodissociateeffectsizeandeffectprevalence |